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VEGA: An Active-tuning Learned Index with Group-Wise Learning Granularity

Summary: VEGA uses active-tuning with group-wise granularity to simplify distribution and tighten lookup bounds. A memory-budget framework merges key grouping with online key repositioning to achieve strong theory and empirical lookup/build performance. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h6b9230cd96c4dcc4
Venue
SIGMOD
Year
2025
Pagerank
4.9769913e-05
Overall Rank
11,131 | 25.19%
DOI
10.1145/3709736

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Authors

BibTeX Citation

@inproceedings{li_sigmod25,
        title = {{VEGA: An Active-tuning Learned Index with Group-Wise Learning Granularity}},
        author = {Li, Meng and Chai, Huayi and Luo, Siqiang and Dai, Haipeng and Gu, Rong and Zheng, Jiaqi and Chen, Guihai},
        series = {{SIGMOD} '25},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3709736},
        url = {https://dl.acm.org/doi/10.1145/3709736},
        year = {2025}
}

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Outgoing Citations (Sorted by Pagerank)

Showing 25 of 25 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046363107
68 Making B+-Trees Cache Conscious in Main Memory 2000 SIGMOD 0.00037957995
206 Cache Conscious Indexing for Decision-Support in Main Memory 1999 VLDB 0.00024981343
229 A Study of Index Structures for Main Memory Database Management Systems 1986 VLDB 0.00023915204
277 FAST: Fast Architecture Sensitive Tree Search on Modern CPUs and GPUs 2010 SIGMOD 0.00022320139
422 ALEX: An Updatable Adaptive Learned Index 2020 SIGMOD 0.00018488849
458 The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds 2020 VLDB 0.00017880664
547 Improving Index Performance through Prefetching 2001 SIGMOD 0.00016575635
768 FITing-Tree: A Data-aware Index Structure 2019 SIGMOD 0.00014107655
835 Benchmarking Learned Indexes 2021 VLDB 0.00013575971
960 Reducing the Storage Overhead of Main-Memory OLTP Databases with Hybrid Indexes 2016 SIGMOD 0.0001283613
1,525 Updatable Learned Index with Precise Positions 2021 VLDB 0.00010355133
2,270 FINEdex: A Fine-grained Learned Index Scheme for Scalable and Concurrent Memory Systems 2022 VLDB 8.7166469e-05
2,582 Are Updatable Learned Indexes Ready? 2022 VLDB 8.2641447e-05
3,198 CDFShop: Exploring and Optimizing Learned Index Structures 2020 SIGMOD 7.5422544e-05
3,710 CARMI: A Cache-Aware Learned Index with a Cost-based Construction Algorithm 2022 VLDB 7.0775695e-05
4,362 NFL: Robust Learned Index via Distribution Transformation 2022 VLDB 6.6357559e-05
4,366 DILI: A Distribution-Driven Learned Index 2023 VLDB 6.6330579e-05
4,618 The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures 2022 SIGMOD 6.4981355e-05
5,378 Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices 2023 SIGMOD 6.1551053e-05
5,405 FILM: a Fully Learned Index for Larger-than-Memory Databases 2023 VLDB 6.1435108e-05
6,267 A Critical Analysis of Recursive Model Indexes 2022 VLDB 5.8283218e-05
6,613 Making In-Memory Learned Indexes Efficient on Disk 2024 SIGMOD 5.7326443e-05
6,787 SALI: A Scalable Adaptive Learned Index Framework based on Probability Models 2023 SIGMOD 5.6810904e-05
6,867 Accelerating String-key Learned Index Structures via Memoization-based Incremental Training 2024 VLDB 5.6581046e-05
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